LOSTU: Fast, Scalable, and Uncertainty-Aware Triangulation

arXiv:2311.11171v2 Announce Type: replace
Abstract: This work proposes a non-iterative, scalable, and statistically optimal way to triangulate called \texttt{LOSTU}. Unlike triangulation algorithms that minimize the reprojection ($L_2$) error, LOSTU will still provide the maximum likelihood estimate when there are errors in camera pose or parameters. This generic framework is used to contextualize other triangulation methods like the direct linear transform (DLT) or the midpoint. Synthetic experiments show that LOSTU can be substantially faster than using uncertainty-aware Levenberg-Marquardt (or similar) optimization …

abstract algorithms arxiv cs.cv error errors framework iterative likelihood parameters scalable type uncertainty will work